Through the Lens of Mechanisms: How neurovascular coupling unknowns limit fMRI interpretations
Poster No:
1391
Submission Type:
Abstract Submission
Authors:
Gang Chen1, Zhengchen Cai2, Paul Taylor1
Institutions:
1National Institutes of Health, Bethesda, MD, 2McGill University, Montreal, Quebec
First Author:
Co-Author(s):
Introduction:
Resting-state fMRI often examines cross-regional correlations to explore their associations with behavioral and phenotypic data. Another popular avenue involves treating correlations as graphical entities (e.g., edges), deriving network features through graph theory to infer brain network properties.
We focus on a central challenge: how robustly can fMRI reveal causal relationships between regions? Despite noise-reduction techniques, fMRI's reliance on the BOLD signal as a proxy for underlying neural activity imposes inherent limitations, complicating claims of causality and correlation. This work underscores the critical need for methodological rigor and a nuanced understanding of fMRI's constraints.
We focus on a central challenge: how robustly can fMRI reveal causal relationships between regions? Despite noise-reduction techniques, fMRI's reliance on the BOLD signal as a proxy for underlying neural activity imposes inherent limitations, complicating claims of causality and correlation. This work underscores the critical need for methodological rigor and a nuanced understanding of fMRI's constraints.
Methods:
Resting-state fMRI data are susceptible to various noise sources that can bias correlation estimates. Two sources of fMRI signal imperfections pose challenges: (i) proxy role of fMRI for neural activity, due to the complexity of neurovascular coupling; (ii) impact of non-neural noises. The resulting biases in correlations include underestimation, spurious correlations, or overestimation, distorting the estimation of the presence/absence of true neural synchrony. To explore these effects, we simulated two brain regions with 300 time points (representing a 10-minute scan with a TR of 2 seconds) across two groups of 20 individuals.
Graph-based analysis, which relies on nodes (brain regions) and edges (correlations), is widely used to infer network-level properties. However, inaccuracies in correlation estimates propagate, jeopardizing the reliability of derived network features. Using causal inference [1], analytical models were constructed to assess the causal relationships between regions while accounting for noise. We aim to address the following three questions:
* Does a near-zero correlation indicate no information flow?
* Does the sign of a correlation (positive or negative) imply excitatory or inhibitory information flow?
* Can the magnitude of a correlation reliably infer the strength of information flow?
Graph-based analysis, which relies on nodes (brain regions) and edges (correlations), is widely used to infer network-level properties. However, inaccuracies in correlation estimates propagate, jeopardizing the reliability of derived network features. Using causal inference [1], analytical models were constructed to assess the causal relationships between regions while accounting for noise. We aim to address the following three questions:
* Does a near-zero correlation indicate no information flow?
* Does the sign of a correlation (positive or negative) imply excitatory or inhibitory information flow?
* Can the magnitude of a correlation reliably infer the strength of information flow?
Results:
Noise-induced biases in correlations significantly impact downstream analyses, such as group comparisons or associations with behavioral traits. Three major issues emerge:
1) Spurious differences: Differences in noise magnitude, inter-regional noise correlation, or noise-neural interactions can create misleading group differences, even when none are present (Fig. 1A).
2) Suppressed differences: Genuine neural correlations may go undetected due to noise interference (Fig. 1B).
3) Sign flipping: Comparisons can be sign-flipped, leading to incorrect interpretation of the direction of correlation (Fig. 1C).
Graphical features such as edges derived from these biased correlations are equally problematic. For instance:
* Does a near-zero correlation indicate no information flow? Counterexamples in Fig. 2A suggest otherwise.
* Does the sign of a correlation (positive or negative) imply excitatory or inhibitory information flow? Fig. 2B highlights scenarios where this assumption fails.
* Can the magnitude of a correlation reliably infer the strength of information flow? Fig. 2A illustrates the limitations of such inferences.
1) Spurious differences: Differences in noise magnitude, inter-regional noise correlation, or noise-neural interactions can create misleading group differences, even when none are present (Fig. 1A).
2) Suppressed differences: Genuine neural correlations may go undetected due to noise interference (Fig. 1B).
3) Sign flipping: Comparisons can be sign-flipped, leading to incorrect interpretation of the direction of correlation (Fig. 1C).
Graphical features such as edges derived from these biased correlations are equally problematic. For instance:
* Does a near-zero correlation indicate no information flow? Counterexamples in Fig. 2A suggest otherwise.
* Does the sign of a correlation (positive or negative) imply excitatory or inhibitory information flow? Fig. 2B highlights scenarios where this assumption fails.
* Can the magnitude of a correlation reliably infer the strength of information flow? Fig. 2A illustrates the limitations of such inferences.
Conclusions:
Resting-state fMRI correlations provide valuable insights into inter-regional brain dynamics. However, pervasive noise and methodological constraints often compromise their accuracy, leading to significant ambiguities in estimation and interpretation. These challenges extend to network-level inferences, undermining the reliability of causal claims. Addressing these limitations requires advancing neurovascular modeling and refining analytic approaches to distinguish genuine neural interactions from noise-driven artifacts.
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling 1
Methods Development
Other Methods
Keywords:
Data analysis
FUNCTIONAL MRI
Modeling
Statistical Methods
1|2Indicates the priority used for review